A distributed learning based on robust diffusion SGD over adaptive networks with noisy output data

Outliers and noises are unavoidable factors that cause performance of the distributed learning algorithms to be severely reduced. Developing a robust algorithm is vital in applications such as system identification and forecasting stock market, in which noise on the desired signals may intensely div...

Full description

Saved in:
Bibliographic Details
Published in:Journal of parallel and distributed computing Vol. 190; p. 104883
Main Authors: Barani, Fatemeh, Savadi, Abdorreza, Sadoghi Yazdi, Hadi
Format: Journal Article
Language:English
Published: Elsevier Inc 01.08.2024
Subjects:
Adaptive networks
Distributed learning
Non-Gaussian noise
Pseudo-Huber loss
Robust stochastic gradient descent
Adaptive networks
Pseudo-Huber loss
Distributed learning
Robust stochastic gradient descent
Non-Gaussian noise
ISSN:0743-7315, 1096-0848
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Outliers and noises are unavoidable factors that cause performance of the distributed learning algorithms to be severely reduced. Developing a robust algorithm is vital in applications such as system identification and forecasting stock market, in which noise on the desired signals may intensely divert the solutions. In this paper, we propose a Robust Diffusion Stochastic Gradient Descent (RDSGD) algorithm based on the pseudo-Huber loss function which can significantly suppress the effect of Gaussian and non-Gaussian noises on estimation performances in the adaptive networks. Performance and convergence behavior of RDSGD are assessed in presence of the α-stable and Mixed-Gaussian noises in the stationary and non-stationary environments. Simulation results show that the proposed algorithm can achieve both higher convergence rate and lower steady-state misadjustment than the conventional diffusion algorithms and several robust algorithms. •Providing a good framework to manage large-scale problems and avoid data aggregation in a central workstation, and also saves time and energy.•Solving distributed learning problems by applying diffusion strategies.•Presenting a robust distributed algorithm based on diffusion strategies of the SGD type to manage large-scale problems.•Investigating the convergence behavior of distributed algorithm in presence of non-Gaussian noises.•Studying the performance analysis of the distributed algorithm into mean convergence and stability.
ISSN:0743-7315
1096-0848
DOI:10.1016/j.jpdc.2024.104883